From Data to Action: How Asset Health Monitoring with Machine Learning Prevents Failures and Creates a Competitive Edge

Kensan & Weidmüller demonstrate how machine builders can use Asset Health Monitoring and Machine Learning to prevent failures and market their machines effectively. By monitoring machine data in real time and analyzing it with smart algorithms (ML), deviations are identified early, causes of unplanned downtime are made visible, and service and maintenance processes are made predictable (Predictive Maintenance).
End users will discover how this approach can be applied directly to both new and existing production environments, leading to demonstrable improvements in availability, hidden costs, and OEE (Overall Equipment Effectiveness). This provides both machine builders and end customers with practical tools to leverage operational data in real time for improved decision-making on the shop floor and in strategic investments.
In short: an inspiring and practical session for machine builders and end users to reduce downtime and realize distinctive, data-driven innovation.

Wiek Wijnands – Kensan on behalf of Weidmüller Benelux

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